Computational Oceanography + Climate @ NYU
Computational Oceanography + Climate @ NYU
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A. Adcroft
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Machine learned equations for vertical mixing coefficients in the ocean surface boundary layer
Samudra 2: Scaling Ocean Emulators across Resolutions
Calibration of a neural network ocean closure for improved mean state and variability
FloeNet: A mass-conserving global sea ice emulator that generalizes across climates
Advancing global sea ice prediction capabilities using a fully coupled climate model with integrated machine learning
Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models
Design and implementation of a data-driven parameterization for mesoscale thickness fluxes
Addressing Out-of-Sample Issues in Multi-Layer Convolutional Neural-Network Parameterization of Mesoscale Eddies Applied Near Coastlines
Samudra: An AI Global Ocean Emulator for Climate
A Stable Implementation of a Data-Driven Scale-Aware Mesoscale Parameterization
Transfer Learning for Emulating Ocean Climate Variability across CO2 forcing
Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations
Remote Versus Local Impacts of Energy Backscatter on the North Atlantic SST Biases in a Global Ocean Model
Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks
Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model
Deep learning of systematic sea ice model errors from data assimilation increments
NeverWorld2: An idealized model hierarchy to investigate ocean mesoscale eddies across resolutions
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